Deep-MLE: Fusion between a Neural Network and MLE for a Single Snapshot DOA Estimation
| Autor: | Marcio L. Lima de Oliveira, Marco J. G. Bekooij |
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| Přispěvatelé: | Computer Architecture Design and Test for Embedded Systems, Digital Society Institute |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3673-3677 STARTPAGE=3673;ENDPAGE=3677;TITLE=ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Popis: | In this paper, we propose a novel framework called DeepMLE, which gives a solution to the single-snapshot Direction Of Arrival (DOA) estimation problem, up to 4 distinct targets, using a radar equipped with a Minimum Redundancy antenna Array (MRA). This framework works by fusing a Deep Learning (DL) technique - 1D Residual Neural Network (1D ResNet) - with a classical DOA algorithm - Maximum Likelihood Estimation (MLE). By combining two very different approaches, we can address some of their limitations, such as the computational complexity of MLE. On the other hand, our proposed Deep-MLE uses MLE to correct, to some degree, the estimations made by the Neural Network (NN). The results from our framework are promising as it seems to be a viable solution to the DOA estimation problem, having a better performance than models using pure MLE or NN. |
| Databáze: | OpenAIRE |
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